Open Access
E3S Web Conf.
Volume 64, 2018
2018 3rd International Conference on Power and Renewable Energy
Article Number 08004
Number of page(s) 6
Section Power System and Energy
Published online 27 November 2018
  1. G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader, “Electric energy customer characterisation for developing dedicated market strategies,” 2001 IEEE Porto Power Tech Proc., vol. 1, pp. 371-377, 2001. [Google Scholar]
  2. G. Chicco, “Customer Behaviour and Data Analytics,” 2016 Int. Conf. Expo. Electr. Power Eng., no. Epe, pp. 771-779, 2016. [CrossRef] [Google Scholar]
  3. J. Molina and J. García, “Técnicas de Análisis de Datos,” p. 266, 2006. [Google Scholar]
  4. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Mag., pp. 37–54, 1996. [Google Scholar]
  5. G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader, “Customer Characterization Options for Improving the Tariff Offer,” IEEE Power Eng. Rev., vol. 22, no. 11, p. 60, 2002. [CrossRef] [Google Scholar]
  6. G. Chicco, “Overview and performance assessment of the clustering methods for electrical load pattern grouping,” Energy, vol. 42, no. 1, pp. 68–80, 2012. [CrossRef] [Google Scholar]
  7. G. Chicco, R. Napoli, and F. Piglione, “A Review of Concepts and Techniques for Emergent Customer Categorisation,” pp. 51-58, 2002. [Google Scholar]
  8. R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” New York: John Wiley, Section. p. 654, 2000. [Google Scholar]
  9. J. Valente de Oliveira and W. Pedrycz, Advances in Fuzzy Clustering and its Applications Advances in Fuzzy Clustering and its Applications. 2007. [CrossRef] [Google Scholar]
  10. X. Serrano-Guerrero, G. Escrivá-Escrivá, and C. Roldán-Blay, “Statistical Methodology to Assess Changes in the Electrical Consumption Profile of Buildings,” Energy Build., vol. 164, pp. 99–108, 2018. [Google Scholar]
  11. Consejo Nacional de Electricidad, “Estudio y gestión de la demanda eléctrica,” Plan Maest. Electrif. 2013-2022, vol. 2, pp. 41-42, 2013. [Google Scholar]
  12. G. Chicco, R. Napoli, and F. Piglione, “Application of clustering techniques to load pattern-based electricity customer classification,” no. June, pp. 6-9, 2005. [Google Scholar]
  13. M. Piao, J. B. Lee, H. S. Shon, E. J. Cha, K. Ah Kim, and K. H. Ryu, “Identification of temporal interval relation of frequent patterns during incremental phase,” Legacy, pp. 497–502, 2011. [Google Scholar]
  14. G. K. Gupta, Introduction To Data Mining With Case Studies. PHI Learning Pvt. Ltd., 2014. [Google Scholar]
  15. G. Chicco, R. Napoli, and F. Piglione, “Application of Clustering Algorithms and Self Organising Maps to Classify Electricity Customers,” 2003 IEEE Bol. PowerTech - Conf. Proc., vol. 1, pp. 373-379, 2003. [Google Scholar]
  16. MathWorks, “k-Means Clustering,” 2017. [Online]. Available: [Google Scholar]
  17. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012. [Google Scholar]
  18. S. M. H. Jansen, “Customer Segmentation and Customer Profiling for a Mobile Telecommunications Company Based on Usage Behavior,” Proc. - 3rd Int. Conf. Data Min. Intell. Inf. Technol. Appl. ICMIA 2011, no. July, pp. 308-313, 2007. [Google Scholar]
  19. P.-N. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining. 2012. [Google Scholar]
  20. J. J. González, “El histograma con la TI-92: optimización de clases,” Revista de Didáctica de las Matemáticas, vol. 61. pp. 67–72, 2005. [Google Scholar]
  21. P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, no. C, pp. 53-65, 1987. [Google Scholar]
  22. F. Scarlatache, G. Grigora, G. Chicco, and G. Câr, “Using k-Means Clustering Method in Determination of the Optimal Placement of Distributed Generation Sources in Electrical Distribution Systems,” IEEE Optim. Electr. Electron. Equip. (OPTIM), 2012 13th Int. Conf., pp. 953-958, 2012. [CrossRef] [Google Scholar]
  23. MathWorks, “silhouette,” 2017. [Online]. Available: [Google Scholar]
  24. D. M. J. Tax, “One-class classification,” 2001. [Google Scholar]
  25. X. Serrano-Guerrero, R. Prieto-Galarza, E. Huilcatanda, J. Cabrera-Zeas, and G. Escrivá-Escrivá, “Election of Variables and Short-term Forecasting of Electricity Demand Based on Backpropagation Artificial Neural Networks,” IEEE Int. Autumn Meet. Power, Electron. Comput., 2017. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.